Development of a Scalable Edge-Cloud Computing Based Variable Rate Irrigation Scheduling Framework

Currently, variable-rate precision irrigation (VRI) scheduling methods require large amounts of data and processing time to accurately determine crop water demands and spatially process those demands into an irrigation prescription. Unfortunately, irrigated crops continue to develop additional water stress when the previously collected data is being processed. Machine learning is a helpful tool, but handling and transmitting large datasets can be problematic; more rural areas may not have access to necessary wireless data transmission infrastructure to support cloud interaction. The introduction of “edge-cloud” processing to agricultural applications has shown to be effective at increasing data processing speed and reducing the amount of data transmission to remote processing computers or base stations. In irrigation in particular, edge-cloud computing has so far had limited implementation. Therefore, an initial logic flow concept has been developed to effectively implement this new processing technique for VRI. Utilizing edge-cloud computer nodes in the field, autonomous data collection devices such as center pivot-mounted infrared canopy thermometers, soil moisture sensors, local weather stations, and UAVs could transmit highly localized crop data to the edge-cloud computer for processing. The edge computer Following the implementation of an irrigation strategy created by the edge-cloud computer with a machine learning model, data would be transmitted to the cloud (requiring transmission of only minimal model parameters), resulting in a feedback loop for continual improvement of the global model on the cloud (federated learning). VRI prescription maps from the SETMI model were used as the training data for training the machine learning model

Relacion Entre La Densidad Óptima Agronomica Y El Número De Granos Por Planta En Maíz (Zea Maysl.)

The density of sowing (D) is one of the main management practices that influences the yield (Y) of corn. There exists a density value in which the yield is maximum (OPD), depending on the environment, the genotype and its interaction. The objectives of this project were: i-To determine the OPD for two corn genotypes in different productive environments; ii- Analyze the relationship between the number of kernel fixed per plant (KNP) and its plant growing rate (PGR) to different environments and genotypes iii- Determine the KNP that is related to the OPD for two corn genotypes. Three experiments were carried out in different locations (L) of Córdoba (Argentina) during 2013/14, comparing 2 genotypes (G) in 2 management zones (MZ). The statistical design was random blocks, with a factorial arrangement of subdivided plots, with L, MZ and G being the primary, secondary and tertiary factors, respectively. In addition, 5 D were planted to obtain the relationships that estimate OPD, PGR, KNP and Y. The results indicate that OPD was affected by L; the relationship between PGR and KNP was not modified by the environment, but by G. The PGR coincident with the OPD was modified by the G interaction: L. The OPD the PGR was between 2.74 to 4.81 g d -1 , which were associated with the NGP that varied only between 509 and 603 grains p-1

Field Research Report: Results from the ENREEC VRI Field for the 2021, 2022, and 2023 Crop Seasons

Long-term irrigation management research has been conducted from 2014 to 2023 for corn and soybean at the Eastern Nebraska Research, Extension, and Education Center (ENREEC) Variable Rate Irrigation (VRI) Field located in subhumid east-central Nebraska (in the Lower Platte North Natural Resources District). The objective of this report was to present the overall results from the VRI Field for 2021 to 2023. Across the three growing seasons, there were the following irrigation treatments: Best Management Practice (BMP), 50% BMP, 125% BMP, rainfed, Spatial ET Modeling Interface (SETMI), SDD1, SDD2, machine-learning-based Cyber-Physical System (CPS), a student team recommended rate, and industry trials from Irriga Global’s Aluvio. Results showed that from 2021 to 2023, only 2022 was dry enough to have a significant yield response to irrigation in both corn and soybean. The distribution of precipitation in 2023 resulted in a significant difference in yield for corn but not soybean. Over 9 years of corn production, the mean seasonal irrigation was 4.4 in (for full irrigation treatments), corresponding to a mean yield of 246 bu/ac compared to a mean rainfed yield of 227 bu/ac. For 8 years of soybean research, the average seasonal irrigation was 4.0 in; the mean irrigated soybean yield was 70 bu/ac compared to 66 bu/ac for rainfed plots. The long-term average increase in gross revenue (from irrigation) was 104 $/ac/yr for corn and 46$/ac/yr for soybean

Intensive Soybean Management : an Integrated Systems Approach

Ecological intensification impacted soybean yield, biomass and N uptake. Narrow row spacing, high seeding rate, other best production practices, and balanced nutrition increased partitioning effi ciency for biomass, measured by seed harvest index (HI), grain N, and N HI (NHI). Partial factor productivity of fertilizer (PFPf) increased when best production and fertilizer management practices were implemented in combination, with 19% and 28% increases under irrigated and dryland scenarios, respectively. An integrated approach, simultaneously considering multiple management factors in a farming system, is needed for closing exploitable yield gapsEEA OliverosFil: Balboa, Guillermo R. Kansas State University. Department of Agronomy; Estados UnidosFil: Stewart, W.M. International Plant Nutrition Institute. North American Program; Estados UnidosFil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros. Departamento de Agronomía; ArgentinaFil: García, Fernando O. International Plant Nutrition Institute. Latin American Southern Cone; ArgentinaFil: Francisco, Eros Artur Bohac. International Plant Nutrition Institute; BrasilFil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados Unido

Nutrient partitioning and stoichiometry in soybean: A synthesis-analysis

On-farm attainable soybean yields are primarily limited by nutrient and water supply. High-yielding soybeans is related to high nutrient uptake. A proposed theoretical framework underpinning yield formation includes plant nitrogen (N) uptake, N harvest index (NHI), and N seed concentration (%Nseeds). The objectives of this study were focused on (i) investigating the effect of NHI and %Nseed on yield-to-uptake relation for N, and (ii) analyzing dry mass and N partitioning and extending this analysis to phosphorous (P) and potassium (K) uptake and (iii) studying the influence of specific seed:stover ratios on the relationship of N with P, and K uptake. Metadata on yield, nutrient uptake and specific-organ nutrient concentration (%nutrient) was summarized from experiments located in three different environments: Indiana, Kansas (both US), and Argentina (herein termed as IN, KS, and ARG, respectively). The main outcomes from this research were: 1) the yield-to-uptake relation for N was primarily explained by NHI; 2) the algebraic model proposed by Sinclair (1998), that includes each specific-organ %nutrient explained consistently nutrient (N, P or K) HI as a function of HI with different trend, and 3) plant nutrient ratios were primarily governed by vegetative %nutrient (stover fraction), acting as a nutrient reservoir or supply depending on the demand of nutrient in the seed. Further research on the nutrient and biomass partitioning should focus on examining the NHI:HI relationship under varying genotype x environment x management interaction.EEA OliverosFil: Tamagno, S. Kansas State University. Department of Agronomy; Estados UnidosFil: Balboa, Guillermo R. Kansas State University. Department of Agronomy; Estados UnidosFil: Assefa, Y. Kansas State University. Department of Agronomy; Estados UnidosFil: Kovács, P. Purdue University. Department of Agronomy; Estados UnidosFil: Casteel, S.N. Purdue University. Department of Agronomy; Estados UnidosFil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros. Departamento de Agronomía; ArgentinaFil: García, Fernando O. International Plant Nutrition Institute. Latin American Southern Cone; ArgentinaFil: Stewart, W.M. International Plant Nutrition Institute. Great Plains Region; Estados UnidosFil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados Unido

A systems-level yield gap assessment of maize-soybean rotation under high- and low-management inputs in the Western US Corn Belt using APSIM

Quantifying yield gaps (potential minus actual yield) and identifying management practices to close those gaps is critical for sustaining high-yielding production systems. The objectives of this study were to: 1) calibrate and validate the APSIM maize and soybean models using local field experimental data and 2) use the calibrated model to estimate and explain yield gaps in the long term as a function of management (high- vs low-input) and weather conditions (wet-warm, wet-cold, dry-warm and dry-cold years) in the western US Corn Belt. The model was calibrated and validated using in-season crop growth data from six maize-soybean rotations obtained in 2014 and 2015 in Kansas, US. Experimental data included two management systems: 1) Common Practices (CP, low-input), wide row spacing, lower seeding rate, and lack of nutrient applications (except N in maize), and 2) Intensified Practices (IP, high-input), narrow rows, high seeding rate, and balanced nutrition. Results indicated that APSIM simulated in-season crop above ground mass and nitrogen (N) dynamics as well yields with a modeling efficiency of 0.75 to 0.92 and a relative root mean square error of 18 to 31%. The simulated maize yield gap across all years was 4.2 and 2.5 Mg ha−1 for low- and high-input, respectively. Similarly, the soybean yield gap was 2.5 and 0.8 Mg ha−1. Simulation results indicated that the high-input management system had greater yield stability across all weather years. In warm-dry years, yield gaps were larger for both crops and water scenarios. Irrigation reduced yield variation in maize more than in soybean, relative to the rainfed scenario. Besides irrigation, model analysis indicated that N fertilization for maize and narrow rows for soybean were the main factors contributing to yield gains. This study provides a systems level yield gap assessment of maize and soybean cropping system in Western US Corn Belt that can initiate dialogue (both experimental and modeling activities) on finding and applying best management systems to close current yield gaps.EEA OliverosFil: Balboa, Guillermo R. Kansas State University. Department of Agronomy; Estados Unidos. Universidad Nacional de Río Cuarto; ArgentinaFil: Archontoulis, Sotirios. Iowa State University. Department of Agronomy; Estados UnidosFil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros. Departamento de Agronomía; ArgentinaFil: García, Fernando O. International Plant Nutrition Institute. Latin American Southern Cone; ArgentinaFil: Stewart, W.M. International Plant Nutrition Institute. Great Plains Region; Estados UnidosFil: Francisco, Eros Artur Bohac. International Plant Nutrition Institute. Cerrados; BrasilFil: Vara Prasad, P.V. Kansas State University. Department of Agronomy; Estados UnidosFil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados Unido

Integrating UAV hyperspectral data and radiative transfer model simulation to quantitatively estimate maize leaf and canopy nitrogen content

Crop nitrogen (N) content reflects crop nutrient status and plays an important role in precision nutrient management. Accurate crop N content estimation from remote sensing has been well documented. However, the robustness (i.e., the ability of a model to perform consistently across various conditions) of these methods under varied soil conditions or different growth stages has rarely been considered. We proposed a hybrid method that integrates in-situ measurements and the data simulated by a mechanistic model to improve the estimation of maize N content. In-situ data included hyperspectral images collected by Unmanned Aerial Vehicle (UAV), and leaf and canopy N content (LNC and CNC). A mechanistic radiative transfer model (PROSAIL-PRO) was used to generate simulated data, i.e., canopy reflectance paired with target crop traits (i.e., LNC, CNC). We compared the performance from the hybrid method with a machine learning method (Gaussian Process Regression) and six different vegetation indices (VIs) on four in-situ datasets collected at three study sites from 2021 to 2022. Results show that the hybrid method consistently performed the best for LNC estimation across four testing datasets (RRMSE ranging from 10.08% to 10.84%). For CNC estimation, the hybrid method had the best estimation results on two out of the four testing datasets and performed comparably to the best method on the other two datasets (RRMSE ranging from 13.89% to 25.21%). Next, we assessed the estimation robustness of the hybrid method, the machine learning, and the best-VI by comparing the mean (µ) and standard deviation (σ) of RRMSE across diverse water and N treatments (condition #1) and different growth stages (condition #2). Among 16 total cases (two crop traits by four study sites by two conditions), the hybrid method had 11 cases of smallest µ and seven cases of smallest σ, outperforming the machine learning (0/16 for µ, 4/16 for σ) and the best-VI (5/16 for µ, 5/16 for σ). These results underscore the greater robustness of the hybrid method. This study highlights the potential of integrating in-situ measurements and simulated data to improve estimation accuracy and robustness for maize LNC and CNC. The promising performance of the hybrid method suggests its applicability to a broader range of crops and various crop traits